This invention relates to a method for automatic classification of a fingerprint.
Fingerprint identification can be employed in identifying individuals in an access control system, a electronic transaction system or other applications. Fingerprint classification is generally used as a pre-processing of the fingerprint identification which has a high computational complexity. Classifying the fingerprints into various classifications can narrow down the search and comparison space considerably, thus reducing the time of identification and increasing the accuracy at the same time.
Few commonly know approaches to automatic classification of fingerprints include structural, syntatic, rule based and artificial neural network. These approaches may adopt different systems of classification. For instance, in U.S. Pat. No. 5,337,369, fingerprints are classified into 5 different classes, namely, whorl, right loop, left loop, tented arch and plain arch. This invention classifies the fingerprints into a total of 8 classes, namely, right loop, left loop, tented arch, plane arch, right twin, left twin, whorl and others. There is usually a trade-off between the number of classes and the accuracy and speed of processing. However, the number of classes a method can identify will determine the practical value of the method.
The methods of automatic fingerprint classification generally begins with the identification of singularity points, e.g., core and delta points, on the fingerprint. Based on the singularity points, various methods are used further to determine the classification of the fingerprint. Note, however, that some fingerprints do not have a delta point. In such cases, those classification methods based on both core and delta points will fail. The method used in this invention only relies on the core points and the ridge flow direction around these core points to classify the fingerprints. The method disclosed in U.S. Pat. No. 5,337,369 is similar to the algorithm in this invention that it also analyses the ridge flow around the core to classify a fingerprint. However, the disclosed method only makes use of one core point in a fingerprint to classify it, thus not taking advantage of the presence of more than one core point for fingerprint classification. Besides, it only discriminates five different classes of fingerprints which may be too few in practice.